Robust Localization for Underground Mining Vehicles: An Application in a Room and Pillar Mine.

Area of Science:

• Robotics and Autonomous Systems
• Geospatial Mapping and Surveying
• Mining Engineering

Background:

• Current autonomous navigation in underground mining relies on 2D LIDAR and maps, which struggle with environments featuring significant elevation changes.
• Existing 2D mapping methods often fail to produce accurate representations of complex underground mine environments.

Purpose of the Study:

• To propose and validate a novel methodology for generating and updating 2D grid maps of underground tunnels using 3D LIDAR and 3D SLAM in conjunction with 2D mapping techniques.
• To overcome the limitations of existing 2D-only mapping systems in accurately representing environments with varying elevations.

Main Methods:

• Integration of 3D LIDAR data and 3D Simultaneous Localization and Mapping (SLAM) algorithms with traditional 2D mapping methods.
• Development of an enhanced 2D map-building process capable of handling significant elevation variations in underground tunnels.

Main Results:

• The proposed enhanced 2D map-building method demonstrated superior mapping performance compared to conventional 2D-only approaches.
• The generated 2D maps facilitated robust 2D localization for autonomous vehicles.
• Successful testing of the methodology in a real-world operational environment at the Werra Potash Mine, Germany.

Conclusions:

• The combined use of 3D LIDAR and 3D SLAM significantly improves the accuracy and robustness of 2D map generation for underground mines.
• The enhanced mapping approach enables reliable autonomous navigation and operation of mining vehicles, such as autonomous load-haul-dump (LHD) vehicles, in diverse mining conditions.